I heard for the first time about using Cohort Analysis in Adobe Analytics during the talk “The Chef’s Table” from Ben Gaines at Adobe Summit EMEA in London last May (2016). Ben explained that Cohort Analysis was one of the cool things coming with Workspace.
I immediately thought that it’s an “ingredient” that should be present in any analyst’s table in which meaningful insights are to be prepared.
What is Cohort Analysis?
Wikipedia says that “Cohort analysis is a subset of Behavioral Analytics
that takes the data from a given dataset”.
To put it clear and adapt the definition to the context, I will say that a cohort is a group of users who performed a specific action at the same time.
For example: users who came to our site from a PPC campaign and created an account on the first week of May. That’s the cohort, and the cohort analysis will let us take a digging about i.e. the amount of purchase orders generated by that cohort during the next ten weeks.
It will enable us to segment a bit further very easily, and know some characteristics about those users who actually purchased (what do they have in common?) and those who don’t (again, what do they have in common?)
Why is it important?
Looking at conversions / user behaviour over time, cohort analysis helps us to understand more easily the long term relationship with our users or customers.
We can use Cohort Analysis for business questions like:
– How effective was a PPC campaign aiming for new accounts in terms of orders over time?
– How is the purchasing loyalty for a specific product category?
– What segments (cohorts) generate more revenue over time?
– How is the engagement in terms of returning visits generated by a specific action?
We can now easily evaluate the impact and effectiveness of specific actions (or campaigns etc.) on user engagement, conversion content retention etc.
And last but not least, we can apply segments, so we can focus only on a section, referrer, device etc. that is key for us.
How Cohort Analysis can be done in WorkSpace?
Just go to Workspace and select a project. Then select “Visualizations” and drang “Cohort Table” into a “Freedom Table”
A table will appear containing three elements:
Day, Week, Month etc.
- Inclusion Metric
The metric that places a user in a cohort.
For example, if I choose Create Account, only users who have created an account during the time range of the cohort analysis will be included in the initial cohorts.
- Return Metric
The metric that indicates the user has been retained.
For example, if I choose Orders, only users who performed an order after the period in which they were added to a cohort will be represented as retained.
The inclusion and return metric can be the same. For example Orders, to watch the purchasing loyalty.
Cohort Analysis in place
I am changing the example to just visits. Among the users who visited us in a specific day, how many of them returned during the next secuencial days? (remember that this question can be about orders or any other metric/s)
For the question above, I have the table below
Every cell, depending on if the numbers are bigger or smaller, show a brighter or softer green colour. And we can be “curious” about both groups (what seems to be working well, to take a digging about «why» who etc. and same thing to understand what seems not to be working as expected)
To know what have in common the users behind a specific cell we want to analyste further, just right click on a specific cell and it will open the segment manager tool containing the cohort.
Save the segment and take a digging, looking to the entry page, referrer, devices etc. of this specific cohort in which in the example we are not engaging, to know more about that specific type of user
Cohort Analysis can help to analyse the long run. It´s very common analysing how many sales have been generated by the different campaigns and «that´s all». But Cohort Analysis can help us to know what happened over time with customers who bought for the first time from campaign a and the ones who did for b, o who purchased product type a or b.
And that´s an improvement 🙂
And you? How do you watch your key segments over the time?
Any idea? Any comment? Any complaint? 🙂 Leave your comment and I will get back to you. You can also contact with me via email firstname.lastname@example.org o through my Linkedin and Twitter profiles.